Automatic Generation of Fuzzy Classification Rules from Data

Abstract

In this paper, we propose a method for automatic generation of fuzzy rules for data classification. The proposed method is based on subtractive clustering optimized using genetic algorithm. It searches for the FIS structure and number of rules that have the highest fitness value. Multiple performance measures are incorporated into the fitness function to address the problem of imbalanced data. Fitness function includes both training and validation to avoid data over-fitting. Classification performance of the proposed method is evaluated using different data sets and results are compared to those of a number of models generated by fuzzy cmeans clustering with various cluster numbers. Results show that the proposed method has better accuracy and a well compromised sensitivity and specificity.